Recent years have witnessed the resurgence of knowledge engineering which is featured by the fast growth of knowledge graphs. However, most of existing knowledge graphs are represented with pure symbols, which hurts the machine's capability to understand the real world. The multi-modalization of knowledge graphs is an inevitable key step towards the realization of human-level machine intelligence. The results of this endeavor are Multi-modal Knowledge Graphs (MMKGs). In this survey on MMKGs constructed by texts and images, we first give definitions of MMKGs, followed with the preliminaries on multi-modal tasks and techniques. We then systematically review the challenges, progresses and opportunities on the construction and application of MMKGs respectively, with detailed analyses of the strength and weakness of different solutions. We finalize this survey with open research problems relevant to MMKGs.
翻译:近些年来,以知识图的快速增长为特征的知识工程重新出现,然而,大多数现有知识图都以纯符号表示,这损害了机器了解现实世界的能力。知识图的多模式化是实现人类一级机器智能的一个不可避免的关键步骤。这项工作的结果是多模式知识图(MMKGs)。在对以文字和图像制作的MMKGs的调查中,我们首先给出MMKGs的定义,然后是多模式任务和技术的预备性研究。然后,我们系统地审查关于MMKGs的构建和应用的挑战、进展和机会,并详细分析不同解决办法的强弱。我们最后用与MKGs有关的公开研究问题来完成这项调查。